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Shaping innovative services: Reflecting on current and future practice
66
JCPSLP
Volume 19, Number 2 2017
Journal of Clinical Practice in Speech-Language Pathology
to manage large caseloads, establish baseline language
performance, plan and implement intervention, and
demonstrate effectiveness of intervention. Dynamic data
collection and analysis inform whether students can be
discharged to mainstream schooling or whether their needs
are best addressed at the LDC, and therefore as clinicians
we regularly reflect on ways to improve the efficiency and
effectiveness of our practices.
Tools for evaluating language
performance
In order to establish baseline performance, SLPs can select
from a number of tools available to assess language.
Norm-referenced tests allow SLPs to compare children with
age-matched peers in order to identify the presence of
language disorders, whereas criterion-referenced tools
measure a child’s performance of a particular linguistic skill
in reference to a priori criterion of success (Paul & Norbury,
2012). Though norm-referenced assessments are useful for
diagnosis, they are often limited in their capacity to measure
change and lack cultural relevance for certain populations
(Danahy Ebert & Scott 2014; Shipley & McAfee 2009).
Therefore, one must also consider use of criterion-
referenced tools such as language sample analysis (LSA).
LSA supports evaluation of a child’s language
performance in a naturalistic manner. LSA thus enables
clinicians to collect and analyse data that represent
linguistic performance across a range of real-life and
structured communication tasks (Price, Hendricks. & Cook,
2010). It also allows SLPs to acquire data across a range
of different genres and purposes that may be considered
more ecologically valid (Dunn, Flax, Sliwinski, & Aram,
1996). Furthermore, criterion-referenced tools such as
LSA allow improvement in targeted skills to be evaluated
in a dynamic way throughout intervention; in other words
it is not as constrained as standardised norm-referenced
tests regarding test-retest intervals (Paul & Norbury, 2012).
Measuring oral language functioning by systematically
analysing language samples for relevant criteria is often
considered best-practice (Heilmann, Miller, Nockerts &
Dunaway, 2010; Price et al., 2010).
Narrative language sampling
Within a school context, a range of genres may be sampled
and analysed (Whitworth, Claessen, Leitão, & Webster,
2015); however, the importance of narrative performance is
well recognised in the literature (Danahy Ebert & Scott,
Samuel Calder
(top), Cindy
Stirling (centre)
and Laura
Glisson
THIS ARTICLE
HAS BEEN
PEER-
REVIEWED
KEYWORDS
DEVELOPMEN-
TAL LANGUAGE
DISORDER
LANGUAGE
SAMPLE
ANALYSIS
NARRATIVE
SALT
SCHOOL
Language sample analysis is a useful method
of evaluating children’s language
performance. Computer-aided systems such
as Systematic Analysis of Language
Transcription (SALT) can serve to alleviate
constraints clinicians face when analysing
language samples to inform clinical decision-
making. This article describes an initiative
undertaken by a team of speech-language
pathologists in a school context to enhance
the efficiency and comprehensiveness of
analysis of a narrative retell task in a sample
of 131 children with developmental language
disorder, using SALT. We report on the
practicality of using SALT in this school
context, and reflect on our experiences using
the tool. We conclude that SALT is a valuable,
evidence-based tool that enhances
intervention planning and outcome
measurement within the school-based
clinical setting, and offers insights into future
directions involving the use of systematic
analysis of language transcripts within teams.
D
emonstrating the effectiveness of services is
challenging for all speech-language pathologists
(SLPs). This paper reports on the process of
systematic language sample analysis adopted by a team of
SLPs employed in a Language Development Centre (LDC),
a school for children with developmental language disorder
(DLD). Intervention is provided at a classroom level in this
setting; however, measuring children’s individual progress in
addition to cohort-level outcomes is particularly important
as each child’s placement within the specialist language
centre is reviewed every year. As of 2017, the centre caters
for approximately 260 students, with 23 teachers and 15
education assistants to provide classroom level intervention.
A team of five SLPs operate within a responsiveness to
intervention model (Gillam & Justice, 2010), providing
direct specialised support to students at the whole class
(Tier 1), small group (Tier 2) or individual level (Tier 3), or
through consultation with educators in the centre. Given
the large number of students with language support needs,
SLPs at the centre must use time and resources efficiently
Language sample
analysis
A powerful tool in the school setting
Samuel Calder, Cindy Stirling, Laura Glisson, Alannah Goerke, Tina Kilpatrick, Lauren Koch, Anna Taylor,
Robert Wells and Mary Claessen